I'm studying several schemes on classifying patients about their survival time. Let me illustrate the problem with supposing I have just two schemes.
Let's suppose that Scheme 1 put the patients in 5 groups, and Scheme 2 put the patients in 5 groups too (but the groups have different compositions, for example patient 1 in Scheme 1 may be in the group 3 and in Scheme 2 in the group 4).
So, it is my problem but with several schemes.
KM plots are useful to analysis, but it is not practical, and some plots are very similar in visual terms. The logrank test is not the case I think, because I think it is just useful to verify if I really have any difference in the survival experience between the curves and no more.
In really, what I want is to verify which scheme give me a better prediction in some sense of accuracy, in really a measure for that. One way I think was to just use the
survivalROC package and apply the
survivalROC function for times 60, 120 and 180 for example, and what which scheme give the best AUC.
Another way I thought is to use these schemes with cox proportional hazard models, is it really an alternative? Using the
coxph function I can have $R^2$ and concordance in its return, and I can compute the AIC with the
extractAIC function too and choose the best scheme using the less AIC.
In the AIC case I have some doubts beacuse I thought that AIC was good a time ago, but when was I reading recently about it I just found examples with nested models for COX PH, is it applicable to non-nested models too?
So, in summary I thought about 4 options:
survivalROCand use AUC
coxphand use $R^2$
coxphand use concordance
I want to automate my analysis using one numerical measure to automatic selection of the best scheme (remembering the non-nested models nature).
Is there any problem in this approach I am thinking? Which could be the best measure for that?